Am J Cancer Res 2016;6(8):1720-1740 www.ajcr.us /ISSN:2156-6976/ajcr0035266

Original Article The proteome signature of the inflammatory breast cancer plasma membrane identifies novel molecular markers of disease Ivette J Suárez-Arroyo1, Yismeilin R Feliz-Mosquea2, Juliana Pérez-Laspiur3, Rezina Arju4, Shah Giashuddin5, Gerónimo Maldonado-Martínez1, Luis A Cubano1, Robert J Schneider4, Michelle M Martínez-Montemayor1 Universidad Central del Caribe-School of Medicine, Bayamón, PR; 2Inter American University of Puerto Rico, Bayamón, PR; 3Translational Proteomics Center, University of Puerto Rico, San Juan, PR; 4New York University School of Medicine, Alexandria Center for Life Sciences, New York, NY, USA; 5Department of Pathology and Laboratory Medicine, New York Methodist Hospital, New York, NY, USA 1

Received July 7, 2016; Accepted July 10, 2016; Epub August 1, 2016; Published August 15, 2016 Abstract: Inflammatory Breast Cancer (IBC) is the most lethal form of breast cancer with a 35% 5-year survival rate. The accurate and early diagnosis of IBC and the development of targeted therapy against this deadly disease remain a great medical challenge. Plasma membrane proteins (PMPs) such as E-cadherin and EGFR, play an important role in the progression of IBC. Because the critical role of PMPs in the oncogenic processes they are the perfect candidates as molecular markers and targets for cancer therapies. In the present study, Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) followed by mass spectrometry analysis was used to compare the relative expression levels of membrane proteins (MP) between non-cancerous mammary epithelial and IBC cells, MCF-10A and SUM-149, respectively. Six of the identified PMPs were validated by immunoblotting using the membrane fractions of non-IBC and IBC cell lines, compared with MCF-10A cells. Immunohistochemical analysis using IBC, invasive ductal carcinoma or normal mammary tissue samples was carried out to complete the validation method in nine of the PMPs. We identified and quantified 278 MPs, 76% of which classified as PMPs with 1.3-fold or higher change. We identified for the first time the overexpression of the novel plasminogen receptor, PLGRKT in IBC and of the carrier protein, SCAMP3. Furthermore, we describe the positive relationship between L1CAM expression and metastasis in IBC patients and the role of SCAMP3 as a tumor-related protein. Overall, the membrane proteomic signature of IBC reflects a global change in cellular organization and suggests additional strategies for cancer progression. Together, this study provides insight into the specialized IBC plasma membrane proteome with the potential to identify a number of novel therapeutic targets for IBC. Keywords: SILAC, IBC, proteomics, membrane, markers, IDC, SCAMP3, L1CAM

Introduction Inflammatory breast cancer (IBC) is characterized by its rapid and aggressive behavior, where patients have a 43% increased risk of death compared to women with stage-matched nonIBC advanced breast cancer [1]. The hallmark of IBC is the formation of tumor emboli which invade the vascular and lymphatic systems, and are responsible for the inflammatory phenotype, and the high rate of metastasis [2]. Cells comprising tumor emboli maintain aggregation through the overexpression of the transmembrane glycoprotein E-cadherin, forming an overactive complex with alpha/beta-catenin

[3]. Paradoxically, in other types of breast cancer, loss of E-cadherin is associated with an epithelial to mesenchymal transition linked with aggressive tumor invasion and metastasis. Besides the overexpression of E-cadherin, other plasma membrane proteins (PMP) such as EGFR and HER2 are overexpressed in 60% of IBC tumors, both in association with rapid tumor growth rate, invasion and metastasis via the activation of PI3K/AKT and ERK oncogenic pathways [4-6]. Plasma membrane proteins are critical for cell structure, to carry out functions such as membrane-cytoskeleton interactions, extracellular

Plasma membrane proteome of IBC matrix interactions with adjacent cells, sensors of external signals and their downstream intracellular transmission, and as transporters of molecules. Due to their function, PMPs play an important role in oncogenic processes are targets of approximately 70% of cancer therapies in use or under study [7]. A proteomic analysis for membrane protein (MP) identification is a powerful tool used to identify novel biomarkers in breast cancer. Of such methods, SILAC is a simple and accurate approach for identification and quantitation of complex protein mixtures. Many proteomics studies using SILAC have examined the membrane proteome in various cancers, such as breast cancer [7, 8] and lymphoma [9]. Recently, Ziegler et al. examined the PM proteome of several non-IBC cell lines with different molecular subtypes [7]. Results from this study reflected overexpression of tyrosine kinases, cellular adhesion molecules and structural proteins. The accurate and early diagnosis of IBC and the development of targeted therapy against this deadly disease remain a great medical challenge. The identification of membrane proteins from the cell surface and from organelles can shed light on the formation, progression and metastasis processes of IBC. Thus, defining the membrane proteomic profile of IBC has potential for identifying novel molecular markers that will help in the advancement of early diagnosis and subsequent development of therapeutic targets. The present study is the first to identify and quantify the membrane proteome of IBC. This novel study allows characterization and comparison of the PMP profile of the well-studied model of IBC, SUM-149, and non-cancerous mammary epithelial MCF-10A cells. Our data describes the complex image of PMPs present on IBC cells, reflecting the multiple strategies IBC uses to promote highly lymphovascular invasion, and rapid metastatic activity. Materials and methods Cell culture and reagents The patient derived IBC cell line SUM-149 and the SUM-102 cell line were obtained from Dr. Steven Ethier, Medical University of South Carolina Charleston, SC, USA. KPL-4 and MDAIBC-3 cells were kindly provided by Dr. Kurebayashi (Kawasaki Medical School, Japan) and Dr. Wendy Woodward, University of Texas MD Anderson Cancer Center (Houston, TX), respectively. Cells were grown as described 1721

previously [10]. MCF-7 and MCF-10A were obtained from American Type Culture Collection (ATCC) and were cultured in DMEM, 10% FBS or DMEM/F-12 containing 10% Horse Serum, respectively. SILAC™ Protein Identification and Quantitation D-MEM/F-12-Flex Media Kit was purchased from Life Technologies. All kits and developing substrates for immunohistochemistry (IHC) analysis were obtained from Vector Laboratories. Antibodies to C1QBP, Flotilin-1, Metadherin and ITGβ5 were purchased from Cell Signaling Technologies. L1CAM, MCAM and MST1R antibodies were obtained from Abcam. Antibodies to PLGRKT and SCAMP3 were acquired from Sigma. Cell labeling MCF-10A and SUM-149 cells were cultured in 60mm dishes and maintained in their appropriate culture media. To initiate the incorporation of light or heavy labels, 1×105 MCF-10A cells were harvested and suspended in 3 mL of advanced DMEM/F-12-Flex media supplemented with 10% dialyzed FBS, 20 ng/mL EGF, and 0.1 mg/mL heavy lysine ([U13C6] L-lysine and heavy [U13C6] L-arginine. Following the same procedure, SUM-149 cells were suspended in modified DMEM/F-12-Flex supplemented with 10% dFBS and 0.1 mg/mL light L-lysine and light L-arginine. Every three days the media was replaced with the corresponding labeling medium and cells were allowed to expand for at least six doubling times to achieve 99% incorporation of labeled amino acid into the proteins. After six doublings, 106 cells of each cell line were harvested to determine the efficiency of incorporation. 2×106 of each cell line were mixed at 1:1 ratio and lysed on ice for 30 min following the procedure described in [8]. Membrane pellet was dissolved in 20 µL of 4X NuPAGE LDS Sample Buffer containing DTT and heated at 70°C for 10 mins, and analyzed by 1D SDS-NuPAGE and stained with Coomassie Brilliant Blue R-250. For proteomic analysis, each of the cell lines was analyzed in three biologic replicates. Tryptic digestion and peptide fractionation The entire gel lane for each sample was collected and divided in 10 gel sections. Each gel section was subjected to in-gel tryptic digestion by overnight incubation with trypsin in 50 mM NH4HCO3 at 37°C. Digested peptides were then extracted with 60% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA), dried on a speedvac Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC and resuspended in 0.5% TFA. All samples were purified using C18 ZipTips (Millipore) according to manufacturer’s recommendations and resuspended in 2% ACN with 0.1% formic acid prior to LC-MS/MS analysis. LC-MS/MS analysis Sample fractions were dissolved in 25 µL of 2% ACN in 0.1% TFA prior to injection on LC-MS/ MS. A 3.0 µL aliquot was directly injected onto a custom packed 2 cm×100 µm C18 Magic 5 µm particle trap column. Peptides were then eluted and electro sprayed from a custom packed emitter (75 µm×25 cm C18 Magic 3 µm particle) with a linear gradient from 95% solvent A (0.1% FA in water) to 35% solvent B (0.1% FA in ACN) in 35 min at a flow rate of 300 nL/min on a Waters Nano Acquity UPLC system. Data dependent acquisitions were performed on a Q Exactive mass spectrometer (Thermo Scientific) according to an experiment where full MS scans from 300-1750 m/z were acquired at a resolution of 70,000 followed by 12 MS/MS scans acquired under HCD fragmentation at a resolution of 17,500 with an isolation width of 1.2 Da. Data analysis Raw data files were peak processed with Proteome Discoverer (Thermo, v.1.3) prior to searching with Mascot Server (Matrix Sciences Inc., version 2.4) against the SwissProt Human database (v.050113). Search parameters utilized were fully tryptic with 2 missed cleavages, parent mass tolerances of 10 ppm and fragment mass tolerances of 0.05 Da. A fixed modification of carbamidomethyl cysteine and variable modifications of 13C6 on lysine and arginine, acetyl (protein N-term), pyro glutamic for N-term glutamine, oxidation of methionine was considered. Search results were loaded into the Scaffold Viewer (Proteome Software, Inc.) for assessment of protein identification probabilities. SILAC peptides ratios were calculated using the ProteoIQ software (Nusep, Inc., v.2.6). Protein identifications were accepted if they could be established at >90.0% probability and contained at least 2 identified peptides. Tissue samples Breast tissues were kindly provided by Dr. Robert J. Schneider, (NYU, School of Medicine, NY, NY). The Institutional Review Board at NYU approved the informed consent forms for tissue collection. Breast cancer tissues consisted 1722

of 17 IBC and 24 invasive ductal carcinoma (IDC) tumors and 10 normal breast tissues. Immunohistochemistry Paraffin-embedded tissues were deparaffinized, rehydrated and subjected to antigen retrieval using a citrate based solution. Sections were incubated in 5% hydrogen peroxide for 30 min before staining using the Universal Vectastain-ABC horseradish peroxidase kit and incubated with the indicated antibodies. Slides were developed with the DAB substrate kit and counterstained with haematoxylin. We classified the intensity of staining using weak, moderate or strong staining intensities. We identified the percent of stained cells using a quantitative score defined as: “+” less than 10% cells positive staining, “++”, 10-50% cells positive staining and “+++” more than 50% cells positive staining. Location of protein expression was classified as nuclear (N), cytoplasmic (C), nuclear and cytoplasmic (NC), membranous and cytoplasmic (MC) or membranous (M). Immunoblotting Breast cancer cells were lysed and equal total protein was resolved via SDS-PAGE and immunoblotted as described in [11] using the indicated antibodies. Statistical analysis Student’s t-test statistical analyses for immunoblotting studies were done using GraphPad Prism® v.6.0 (San Diego, CA). To analyze the IHC raw data and assess its distribution, univariate statistics, frequencies and percentages were employed. Evaluation for normality assumptions was done prior to the application of any bivariate statistical tests using the Shapiro-Wilk estimate. Differences among group proportions were assessed using Chisquare distribution statistics or Fisher’s exact test. Differences among group means within the patient data were evaluated using an independent samples t-test approach with Levene’s statistic. All analyses were considered significant at P≤0.05. A bivariate association model of seven independent correlation matrices was evaluated using the Pearson product-moment analysis. Each matrix confronted two pairs of possible combinations between: staining, lymphatic invasion, metastasis and invasion. To account for the distribution of all the variables in the dataset, a normality diagnostic test was Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC Table 1. Membrane proteins displaying a 1.3 or higher fold change in differential expression between normal and IBC cells Gene

UniProtKB acceSILAC ratio ssion number

Protein name

Down-regulated Proteins MAN1B1

Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidase

Q9UKM7

-1.7

SLC25A31

ADP/ATP translocase 4

Q9H0C2

-4.5

TBL2

Transducin beta-like protein 2

Q9Y4P3

-1.3

ABCC3

Canalicular multispecific organic anion transporter 2

O15438

1.4

ABCD3

ATP-binding cassette, sub-family D (ALD), member 3

P28288

2.0

ACBD5

Acyl-coa binding domain containing protein 5

Q5T8D3

2.0

AGK

Acylglycerol kinase

Q53H12

1.6

AGPS

Alkylglycerone phosphate synthase

O00116

2.0

AIFM1

Apoptosis-inducing factor 1, mitochondrial

O95831

1.7

ALDH18A1

Delta-1-pyrroline-5-carboxylate synthase

P54886

1.7

ALDH3A2

Fatty aldehyde dehydrogenase

P51648

1.6

APMAP

Adipocyte plasma membrane associated protein

Q9HDC9

1.6

ARL8A

ADP-ribosylation factor-like 8A

Q96BM9

1.3

ATAD1

ATPase family AAA domain-containing protein 1

Q8NBU5

2.2

ATAD3A/ATAD3B

Atpase family, AAA domain containing 3A

Q5T9A4

1.9

ATL3

Atlastin-3

Q6DD88

1.6

ATP13A1

Manganese-transporting ATPase 13A1

Q9HD20

1.3

ATP2A2

Sarcoplasmic/endoplasmic reticulum calcium ATPase 2

P16615

1.4

ATP2A3

ATPase, Ca++ transporting, ubiquitous

Q93084

1.3

ATP5A1

ATP synthase subunit alpha, mitochondrial

P25705

1.8

ATP5B

ATP synthase subunit beta, mitochondrial

P06576

1.8

ATP5C1

ATP synthase subunit gamma, mitochondrial

P36542

1.7

ATP5D

ATP synthase subunit delta, mitochondrial

P30049

1.8

ATP5E

ATP synthase subunit epsilon, mitochondrial

P56381

1.9

ATP5EP2

ATP synthase subunit epsilon-like protein, mitochondrial

Q5VTU8

1.9

ATP5F1

ATP synthase F(0) complex subunit B1, mitochondrial

P24539

1.7

ATP5H

ATP synthase subunit d, mitochondrial

O75947

1.7

ATP5J

ATP synthase-coupling factor 6, mitochondrial

P18859

1.7

ATP5L

ATP synthase subunit g, mitochondrial

O75964

1.8

ATP5O

ATP synthase subunit O, mitochondrial

P48047

1.8

ATP6V0D1

V-type proton ATPase subunit d 1

P61421

1.4

ATP6V1A

V-type proton ATPase catalytic subunit A

P38606

2.1

ATP6V1G1

V-type proton ATPase subunit G 1

O75348

1.7

B3GAT3

Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase3

O94766

2.3

C14orf2

6.8 kDa mitochondrial proteolipid

P56378

2.0

C19orf70

MICOS complex subunit MIC13

Q5XKP0

2.7

C1QBP

Complement component 1, Q subcomponent binding protein

Q07021

2.9

CANX

Calnexin

P27824

2.0

CAP1

Adenylyl cyclase-associated protein 1

Q01518

1.5

CCDC47

Coiled-coil domain containing 47

Q96A33

2.2

CCSMST1

Protein CCSMST1

Q4G0I0

2.0

CERS2

Ceramide synthase 2

Q96G23

1.3

CISD2

CDGSH iron sulfur domain 2

Q8N5K1

1.6

CKMT1A

Creatine kinase U-type, mitochondrial

P12532

1.5

CLCC1

Chloride channel CLIC-like 1

Q96S66

2.3

COQ5

2-methoxy-6-polyprenyl-1,4-benzoquinol methylase, mitochondria

Q5HYK3

2.0

COX20

Cytochrome c oxidase protein 20 homolog

Q5RI15

2.7

COX5A

cytochrome c oxidase subunit 5A

P20674

1.5

COX5B

Cytochrome c oxidase subunit 5B, mitochondrial

P10606

1.3

COX6C

Cytochrome c oxidase subunit 6C

P09669

1.3

Up-regulated Proteins

1723

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Plasma membrane proteome of IBC CPOX

Oxygen-dependent coproporphyrinogen-III oxidase, mitochondrial

P36551

1.5

CPT2

Carnitine palmitoyltransferase 2

P23786

1.7

CTAGE9 (includes others) CTAGE family, member 9

A4FU28

1.3

CYB5B

Cytochrome b5 type B

O43169

1.6

CYC1

Cytochrome c1, heme protein, mitochondrial

P08574

1.5

CYP51A1

Lanosterol 14-alpha demethylase

Q16850

2.0

DAD1

Dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit DAD1

P61803

1.9

DDOST

Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 48 kDa subunit

P39656

2.0

DERL1

Derlin-1

Q9BUN8

1.9

DHRS1

Dehydrogenase/reductase (SDR family) member 1

Q96LJ7

2.0

DNAJC19

Mitochondrial import inner membrane translocase subunit TIM14

Q96DA6

2.4

EBP

3-beta-hydroxysteroid-Delta (8), Delta (7)-isomerase

Q15125

2.1

EMD

Emerin

P50402

1.8

ENO1

Enolase 1, (alpha)

P06733

2.1

ERLIN2

Erlin-2

O94905

1.4

ERO1L

ERO1-like protein alpha

Q96HE7

2.7

ESYT2

Extended synaptotagmin-2

A0FGR8

1.3

FKBP2

Peptidyl-prolyl cis-trans isomerase FKBP2

P26885

2.6

FKBP8

Peptidyl-prolyl cis-trans isomerase FKBP8

Q14318

2.9

FLOT1

Flotillin-1

O75955

1.7

GALNT3

Polypeptide N-acetylgalactosaminyltransferase 3

Q14435

2.8

GALNT7

N-acetylgalactosaminyltransferase 7

Q86SF2

1.7

GBAS

Protein NipSnap homolog 2

O75323

2.5

GOLGA7

Golgin subfamily A member 7

Q7Z5G4

1.7

GOSR1

Golgi SNAP receptor complex member 1

O95249

2.9

GOT2

Aspartate aminotransferase, mitochondrial

P00505

1.8

HK1

Hexokinase-1

P19367

1.6

HK2

Hexokinase-2

P52789

2.0

HLA-A

HLA class I histocompatibility antigen, A-29

P30512

1.9

HLA-A

HLA class I histocompatibility antigen, A-31

P16189

2.0

HLA-A

HLA class I histocompatibility antigen, A-33

P16190

2.1

HLA-A

HLA class I histocompatibility antigen, A-69

P10316

1.7

HLA-A

HLA class I histocompatibility antigen, A-68

P01891

1.5

HLA-A

HLA class I histocompatibility antigen, A-2

P01892

1.7

HLA-E

HLA class I histocompatibility antigen, alpha chain E

P13747

2.1

HSD17B12

Very-long-chain 3-oxoacyl-CoA reductase

Q53GQ0

3.0

HSP90AA1

Heat shock protein HSP 90-alpha

P07900

2.2

HSPA2

Heat shock-related 70 kDa protein 2

P54652

2.1

IFITM3

Interferon induced transmembrane protein 3

Q01628

3.6

IMMT

MICOS complex subunit MIC60

Q16891

2.4

ITGB5

Integrin, beta 5

P18084

2.3

ITPR3

Inositol 1,4,5-trisphosphate receptor type 3

Q14573

1.3

KTN1

Kinectin

Q86UP2

1.5

L1CAM

Neural cell adhesion molecule L1

P32004

1.7

LETM1

Leucine zipper-EF-hand containing transmembrane protein 1

O95202

1.6

LMAN1

Protein ERGIC-53

P49257

2.0

LRPPRC

Leucine-rich PPR motif-containing protein, mitochondrial

P42704

2.6

LRRC59

Leucine rich repeat containing 59

Q96AG4

2.8

M6PR

Cation-dependent mannose-6-phosphate receptor

P20645

2.3

MCAM

Cell surface glycoprotein MUC18

P43121

2.2

MCU

mitochondrial calcium uniporter

Q8NE86

1.5

MFF

Mitochondrial fission factor

Q9GZY8

1.8

MGST1

Microsomal glutathione S-transferase 1

P10620

1.9

MIA3

Melanoma inhibitory activity protein 3

Q5JRA6

1.4

MLEC

Malectin

Q14165

2.0

MMGT1

Membrane magnesium transporter 1

Q8N4V1

2.7

1724

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Plasma membrane proteome of IBC MOGS

Mannosyl-oligosaccharide glucosidase

Q13724

2.5

MSN

Moesin

P26038

1.8

MST1R

Macrophage-stimulating protein receptor

Q04912

1.6

MTDH

Protein LYRIC

Q86UE4

1.7

MTX1

Metaxin-1

Q13505

2.7

MTX2

Metaxin-2

O75431

2.1

MXRA7

Matrix-remodelling associated 7

P84157

2.3

MYO1C

Unconventional myosin-Ic

O00159

1.5

NAPA

Alpha-soluble NSF attachment protein

P54920

1.6

NDUFA3

NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 3

O95167

1.8

NDUFA5

NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 5

Q16718

2.0

NDUFA8

NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 8

P51970

1.9

NDUFB1

NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 1

O75438

1.7

NDUFB10

NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 10

O96000

2.0

NDUFB4

NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 4

O95168

1.4

NDUFB5

NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 5, mitochondrial

O43674

2.3

NDUFB8

NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 8

O95169

1.4

NDUFC2

NADH dehydrogenase [ubiquinone] 1 subunit C2

O95298

1.5

NDUFS2

NADH dehydrogenase [ubiquinone] iron-sulfur protein 2, mitocondrial

O75306

1.5

NDUFS3

NADH dehydrogenase [ubiquinone] iron-sulfur protein 3, mitocondrial

O75489

1.8

NIPSNAP1

Protein NipSnap homolog 1

Q9BPW8

2.3

NOMO1

NODAL modulator 1

Q15155

1.6

NOMO2

Nodal modulator 2

Q5JPE7

1.6

NOMO3

Nodal modulator 3

P69849

1.6

PDIA6

Protein disulfide-isomerase A6

Q15084

3.1

PGAM5

Serine/threonine-protein phosphatase PGAM5, mitochondrial

Q96HS1

1.5

PGRMC1

Progesterone receptor membrane component 1

O00264

2.3

PHB

Prohibitin

P35232

2.2

PHB2

Prohibitin-2

Q99623

2.3

PKP2

Plakophilin-2

Q99959

1.5

PLGRKT

Plasminogen receptor (KT)

Q9HBL7

3.3

PLOD1

Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 1

Q02809

1.7

PLOD2

procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2

O00469

1.4

PLOD3

procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3

O60568

1.4

PNPT1

Polyribonucleotide nucleotidyltransferase 1, mitochondrial

Q8TCS8

2.0

PREB

Prolactin regulatory element binding protein

Q9HCU5

2.2

PTPN1

Tyrosine-protein phosphatase non-receptor type 1

P18031

2.3

RAB14

Ras-related protein Rab-14

P61106

1.7

RAB18

Ras-related protein Rab-18

Q9NP72

1.7

RAB1C

Putative Ras-related protein Rab-1C

Q92928

1.5

RAB2B

Ras-related protein Rab-2B

Q8WUD1

1.4

RAB3D

Ras-related protein Rab-3D

O95716

1.7

RAB6A

Ras-related protein Rab-6A

P20340

2.3

RAB8A

Ras-related protein Rab-8A

P61006

1.7

RAB8B

Ras-related protein Rab-8B

Q92930

1.7

RDH11

Retinol dehydrogenase 11

Q8TC12

2.3

RPN1

Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 1

P04843

1.9

RPN2

Dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 2

P04844

2.1

SCAMP3

Secretory carrier-associated membrane protein 3

O14828

1.6

SCD

Acyl-CoA desaturase

O00767

1.8

SDHA

Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial

P31040

3.0

SDHB

Succinate dehydrogenase [ubiquinone] iron-sulfur subunit, mitochondrial

P21912

2.8

SEC22B

Vesicle-trafficking protein SEC22b

O75396

2.9

SEC61G

Protein transport protein Sec61 subunit gamma

P60059

2.1

SFXN4

Sideroflexin 4

Q6P4A7

2.5

SHMT2

serine hydroxymethyltransferase 2, mitochondrial

P34897

1.5

SIGMAR1

Sigma non-opioid intracellular receptor 1

Q99720

2.8

1725

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Plasma membrane proteome of IBC SLC16A3

Monocarboxylate transporter 4

O15427

1.9

SLC25A11

Mitochondrial 2-oxoglutarate/malate carrier protein

Q02978

2.4

SLC25A19

Mitochondrial thiamine pyrophosphate carrier

Q9HC21

2.9

SLC25A22

Mitochondrial glutamate carrier 1

Q9H936

2.0

SLC25A24

Calcium-binding mitochondrial carrier protein SCaMC-1

Q6NUK1

2.0

SLC25A3

Phosphate carrier protein, mitochondrial

Q00325

2.1

SLC35E1

solute carrier family 35, member E1

Q96K37

1.4

SLC38A10

Putative sodium-coupled neutral amino acid transporter 10

Q9HBR0

3.3

SPCS2

Signal peptidase complex subunit 2

Q15005

2.7

SPCS3

Signal peptidase complex subunit 3

P61009

2.7

SPTLC1

Serine palmitoyltransferase

O15269

1.7

SRPR

Signal recognition particle receptor subunit alpha

P08240

1.5

SSR4

Translocon-associated protein subunit delta

P51571

2.5

SURF4

Surfeit locus protein 4

O15260

1.9

SYNJ2BP

Synaptojanin 2 binding protein

P57105

1.7

SYPL1

Synaptophysin-like 1

Q16563

2.0

TAP1

Antigen peptide transporter 1

Q03518

2.8

TAPBP

Tapasin

O15533

3.2

TIMM23

Mitochondrial import inner membrane translocase subunit Tim23

O14925

2.2

TIMM23B

Putative mitochondrial import inner membrane translocase subunit Tim23B

Q5SRD1

2.2

TIMM44

Mitochondrial import inner membrane translocase subunit TIM44

O43615

2.2

TM9SF1

Transmembrane 9 superfamily member 1

O15321

1.6

TMED2

Transmembrane emp24 domain-containing protein 2

Q15363

1.6

TMED4

Transmembrane emp24 domain-containing protein 4

Q7Z7H5

1.9

TMEM109

Transmembrane protein 109

Q9BVC6

2.0

TMEM205

Transmembrane protein 205

Q6UW68

3.5

TMEM258

Transmembrane protein 258

P61165

2.4

TMEM33

Transmembrane protein 33

P57088

2.2

TMEM43

transmembrane protein 43

Q9BTV4

1.5

TMUB1

Transmembrane and ubiquitin-like domain containing protein 1

Q9BVT8

2.1

TMX1

Thioredoxin-related transmembrane protein 1

Q9H3N1

2.4

TMX3

Protein disulfide-isomerase TMX3

Q96JJ7

1.8

TOMM70A

Mitochondrial import receptor subunit TOM70

O94826

1.9

TPBG

Trophoblast glycoprotein

Q13641

1.4

TRAM1

Translocating chain-associated membrane protein 1

Q15629

2.0

TRAP1

Heat shock protein 75 kDa, mitochondrial

Q12931

2.2

UQCRB

Cytochrome b-c1 complex subunit 7

P14927

1.6

UQCRC1

Cytochrome b-c1 complex subunit 1, mitochondrial

P31930

1.4

UQCRC2

Cytochrome b-c1 complex subunit 2, mitochondrial

P22695

1.4

UQCRFS1

Cytochrome b-c1 complex subunit Rieske, mitochondria

P47985

1.5

USMG5

Up-regulated during skeletal muscle growth protein 5

Q96IX5

1.9

VAMP2

Vesicle-associated membrane protein 2

P63027

1.7

VAMP3

Vesicle-associated membrane protein 3

Q15836

1.7

VAPB

Vesicle-associated membrane protein-associated protein B/C

O95292

2.1

VDAC2

Voltage-dependent anion channel 2

P45880

1.7

VIMP

Selenoprotein S

Q9BQE4

2.0

VMA21

Vacuolar ATPase assembly integral membrane protein VMA21

Q3ZAQ7

3.4

YIPF5

Protein YIPF5

Q969M3

1.4

YIPF6

Protein YIPF6

Q96EC8

2.2

YME1L1

ATP-dependent zinc metalloprotease YME1L1

Q96TA2

2.2

ZDHHC5

Palmitoyltransferase ZDHHC5

Q9C0B5

1.3

ZMPSTE24

CAAX prenyl protease 1 homolog

O75844

2.2

performed using the Shapiro-Francia estimator. The significance level (α) was set to ≤ 0.05, except for the normality diagnostic test 1726

(P>0.05). IBM Statistical Package for Social Sciences (IBM-SPSS, Chicago, IL) v.23.0 for Windows was used. Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC Table 2. Plasma membrane proteins displaying 1.3-fold or higher fold-change in differential expression between normal and IBC cells Gene

Protein name

IFITM3 TMEM205 PLGRKT SLC38A10 PDIA6 C1QBP SIGMAR1 GBAS TMEM258 ITGB5 ATAD1 MCAM PHB TMEM33 ATP6V1A ENO1 HLA-A HLA-A HLA-E SYPL1 HLA-A SLC16A3 ATP5A1 ATP5B ATP5O MSN ATP6V1G1 FLOT1 HLA-A HLA-A L1CAM MTDH RAB18 RAB3D RAB8A RAB8B VAMP2 VAMP3 APMAP MST1R NOMO1 NOMO3 SCAMP3 CAP1 HLA-A KTN1

Interferon induced transmembrane protein 3 Transmembrane protein 205 Plasminogen receptor (KT) Solute Carrier Family 38, Member 10 Protein disulfide-isomerase A6 Complement component 1Q subcomponent binding protein Sigma Non-Opioid Intracellular Receptor 1  Protein NipSnap homolog 2 Transmembrane protein 258 Integrin, beta 5 ATPase family AAA domain-containing protein 1 Cell surface glycoprotein MUC18 Prohibitin Transmembrane protein 33 V-type proton atpase catalytic subunit A Enolase 1, (alpha) HLA class I histocompatibility antigen, A-33 alpha chain HLA class I histocompatibility antigen, A-31 alpha chain HLA class I histocompatibility antigen, alpha chain E Synaptophysin-like 1 HLA class I histocompatibility antigen, A-29 alpha chain Monocarboxylate transporter 4 ATP synthase subunit alpha, mitochondrial ATP synthase subunit beta, mitochondrial ATP synthase subunit O, mitochondrial Moesin V-type proton atpase subunit G 1 Flotillin 1 HLA class I histocompatibility antigen, A-69 alpha chain HLA class I histocompatibility antigen, A-2 alpha chain Neural cell adhesion molecule L1 Protein LYRIC Ras-related protein Rab-18 Ras-related protein Rab-3D Ras-related protein Rab-8A Ras-related protein Rab-8B Vesicle-associated membrane protein 2 Vesicle-associated membrane protein 3 Adipocyte plasma membrane associated protein Macrophage-stimulating protein receptor Nodal modulator 1 Nodal modulator 3 Secretory carrier-associated membrane protein 3 Adenylyl cyclase-associated protein 1 HLA class I histocompatibility antigen, A-68 alpha chain Kinectin

1727

UniProtKB accession number Q01628 Q6UW68 Q9HBL7 Q9HBR0 Q15084 Q07021 Q99720 O75323 P61165 P18084 Q8NBU5 P43121 P35232 P57088 P38606 P06733 P16190 P16189 P13747 Q16563 P30512 O15427 P25705 P06576 P48047 P26038 O75348 O75955 P10316 P01892 P32004 Q86UE4 Q9NP72 O95716 P61006 Q92930 P63027 Q15836 Q9HDC9 Q04912 Q15155 P69849 O14828 Q01518 P01891 Q86UP2

SILAC Ratio 3.6 3.5 3.3 3.3 3.1 2.9 2.8 2.5 2.4 2.3 2.2 2.2 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.0 1.9 1.9 1.8 1.8 1.8 1.8 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.6 1.6 1.6 1.6 1.6 1.5 1.5 1.5

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Plasma membrane proteome of IBC PKP2 ABCC3 ATP6V0D1 RAB2B SLC35E1 TPBG ESYT2 ITPR3 ZDHHC5

Plakophilin-2 Canalicular multispecific organic anion transporter 2 V-type proton atpase subunit d 1 Ras-related protein Rab-2B Solute carrier family 35, member E1 Trophoblast glycoprotein Extended synaptotagmin-2 Inositol 1,4,5-trisphosphate receptor type 3 Palmitoyltransferase ZDHHC5

Q99959 O15438 P61421 Q8WUD1 Q96K37 Q13641 A0FGR8 Q14573 Q9C0B5

1.5 1.4 1.4 1.4 1.4 1.4 1.3 1.3 1.3

Proteins in Bold were selected for validation via IHC or immunoblotting analysis.

Figure 1. Immunoblotting validation of candidate PMPs identified by SILAC. A. Cytoplasmic fraction from MCF-10A and SUM-149 cells. β-actin and E-cadherin were used as controls for cytoplasmic and membrane proteins, respectively. B. Expression of six representatives upregulated PMPs identified by SILAC on IBC and non-IBC cell lines. C. Densitometric analysis using Image J software. β-actin was used as a loading control. Data is expressed relative to MCF-10A cells. Bars represent mean ± SEM of quadruplicates. *P≤0.05.

Results Quantitative analysis of differential membrane proteome expression in IBC SUM-149 vs. MCF10A cells To quantitatively analyze membrane proteome alterations in IBC cells, we performed a SILAC1728

based proteomic analysis. In triplicate experiments we identified and quantified a total of 2,102, 1,869, and 2,002 proteins (false discovery rate ≤1% and at least two identified peptides), respectively, excluding possible sample contaminants (i.e., trypsin, keratins and cytokeratins). Although it is well known that SUM149 cells express cytokeratins 8, 18 and 19, Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC Table 3. Molecular subtypes of cell lines used for SILAC validation Molecular subtype Cell line ER+, PR+, HER2MCF-7 + ER , PR, HER2 , KPL-4, MDA-IBC-3 ER-, PR-, HER2-, EGFR+ MCF-10A, SUM-102, SUM-149 ER: Estrogen Receptor. PR: Progesterone Receptor. HER2: Human Epidermal Growth Factor Receptor 2. EGFR: Epidermal Growth Factor Receptor.

we excluded them in order to decrease false results from contamination by sample handling [12]. By carrying out an analysis of variance, we excluded from the analysis those proteins that were not replicated in the three independent experiments. After performing this assessment, we successfully identified and quantified a total of 634 proteins with 278 (44%) MPs. A change of 1.3-fold (ratio) was used as a cut-off value for significance, which is convention in SILAC proteomic approaches [13]. Among the 278 MPs, 212 (76%) increased at least 1.3-fold and three were downregulated (Table 1). To gain a deeper understanding of the contribution of PMPs to IBC, we grouped 55 proteins in this category showing 1.3-fold or higher upregulation in differential expression between MCF10A and SUM-149 cells (Table 2). Proteins in this category include cell adhesion proteins (MCAM, L1CAM, ITGB5, MTDH), receptors, (MST1R, C1QBP), including the novel membrane plasminogen receptor, PLGRKT. Also, we identified intracellular signaling proteins such as Ras related proteins and transport proteins (SCAMP3, FLOT1, VAMP, ABCC3) among others. Although some listed proteins can exist in multiple cell locations, there is good evidence of their PM association. Although, PMPs such as E-cadherin and EGFR are overexpressed in SUM-149 cells, they are not listed in Table 2. Our raw data (data not shown) did not reveal a difference of 1.3 fold or greater of E-cadherin expression. Meanwhile, the overexpression of EGFR in SUM-149 cells was evidenced in one of our experiments in a ratio of 2.2 [14]. It’s important to highlight that E-cadherin and EGFR have been found overexpressed in IBC when compared with non-IBC breast cancer cells. In our model, we are comparing SUM-149 cells with non-cancerous mammary epithelial cells (MCF-10A) and both cell lines overexpress EGFR.

1729

Validation of SILAC results by immunoblotting To assure that we isolated only membrane proteins, the cytoplasmic fraction from MCF-10A and SUM-149 cells were investigated by SILAC. We assessed the expression of β-actin and E-cadherin as cytoplasmic and membrane protein controls, respectively. We observed expression of β-actin but not of E-cadherin in the cytoplasm, indicating a successful fractionation (Figure 1A). Since the key objective of this study was to identify potential biomarkers for IBC, we selected six biologically important PMPs for validation by immunoblotting (FLOT1, ITGB5, L1CAM, MCAM, MTDH, and PLGRKT). These candidates were chosen because there is no evidence from previous reports demonstrating differential expression or a role in IBC. To confirm the difference in expression between SUM-149 vs. MCF-10A cells, and to assess the expression of these proteins in several breast cancer cell lines, we used IBC (KPL-4, MDAIBC-3 and SUM-149) and non-IBC (MCF-7 and SUM-102) cells with different molecular characteristics (Table 3), compared to MCF-10A cells. Cytoskeleton proteins, such as actins and tubulins were highly represented in our data (data not shown). β-actin has been associated with the plasma membrane for cell organization in the process of cancer proliferation and metastasis [15]. In Figure 1A, β-actin expression was detected in the cytoplasmic cell fraction and was not differentially expressed in SUM-149 cells compared to the MCF-10A PM fraction. For this reason, we chose β-actin as our loading control. SUM-149 cell protein expression was significantly higher for FLOT1 (2.1: P≤0.05), ITGB5 (2.5: P≤0.05), L1CAM (1.9: P≤0.001), MCAM (5.9: P≤0.05), MTDH (1.3: P≤0.01) and PLGRKT (1.6: P≤0.05), in agreement with SILAC results. Furthermore, the IBC KPL-4 cell line also overexpressed FLOT1 (2.1: P≤0.05), ITGB5 (2.7: P≤0.05), and MTDH (1.5: P≤0.05). Non-IBC MCF-7 cells overexpressed FLOT1 (P≤0.05) and L1CAM (P≤0.05). Meanwhile, SUM-102 showed downregulation of ITGB5 (P≤0.05) protein expression (Figure 1B, 1C). Protein expression and cellular distribution The clinical and demographic details of the 17 IBC and 24 IDC patients are shown in Table 4. As expected, IBC patients presented clinically at a younger age than non-IBC patients (P≤0.01). IBC, like non-IBCs, is a heteroge-

Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC Table 4. Clinical and pathological characterization of IBC versus IDC patients Characteristic IBC patients (n=17) IDC patients (n=24) P-valuea Age (years) Range 34-62 39-94 0.0083b Mean ± SD 47.73 ± 9.7 60.38 ± 15.4 ER Positive 9 (52.9%) 17 (70.8%) >0.05 Negative 8 (47.1%) 7 (29.2%) PR Positive 4 (23.5%) 15 (62.5%) 0.025c Negative 13 (76.5%) 9 (37.5%) Her2 Positive 7 (41.2%) 8 (33.3%) Negative 9 (52.9%) 15 (62.5%) >0.05 Unknown 1 (5.9%) 1 (4.2%) Death Dead 8 (47.1%) 10 (41.2%) >0.05 Alive 9 (52.9%) 14 (58.3%) a. Significant P value (P≤ 0.05). b. Student’s t-test. c. Fisher’s exact test.

Table 5. Expression and distribution of validated plasma membrane proteins in tumor and normal breast tissues Type of tissue IBC

Protein

Intensitya

Cells stainedb

Locationc

C1QBP

1/17: Weak 5/17: Moderate 8/17: Strong 3/17: NTT 8/17: Weak 1/17: Moderate 6/17: NS 2/17: NTT 4/17: Weak 10/17: NS 3/17: NTT 6/17: Weak 8/17: Moderate 1/17: Strong 2/17: NTT 6/15: Weak 4/15: Moderate 4/15: Strong 1/15: NTT 4/17: Weak 4/17: Moderate 4/17: Strong 1/17: NS 4/17: NTT 12/17: Weak 2/17: NS 3/17: NTT

14/17: +++

14/17: C

8/17: + 1/17: ++

9/17: C

1/17: + 3/17: +++

2/17: C 2/17: MC

2/17: + 2/17: ++ 11/17: +++

3/17: C 10/17: NC 1/17: MC 1/17: M 14/15: C

L1CAM

MCAM

MST1R

MTDH

PLGRKT

SCAMP3

1730

1/15: + 13/15: +++

1/17: + 3/17: ++ 8/17: +++

8/17: C 4/17: NC

9/17: + 1/17: ++ 2/17: +++

9/17: C 1/17: NC 2/17: M

neous disease and can occur as any of the six molecular breast cancer subtypes. However, IBC as a highly lethal cancer is most commonly triple negative or ER-, PR- and HER2+. Clinical data comparing IBC to IDC patients revealed no significant difference in HER2 or ER status between the two groups, but showed a higher expression of PR in IDCs than IBC patients (P≤0.05). As shown in Table 5 and Figure 2A, 2B, antibodies recognizing C1QBP, L1CAM, MCAM, MST1R, MTDH, PLGRKT and SCAMP3 were used to confirm the results of previous analyses and to assess protein distributions. Eight of seventeen IBC tissues, stained strongly and all normal breast tissues (NBT) stained weakly (10/10) for C1QBP (P50% of cells stained positive with the distribution of C1QBP cytoplasmic in both type of tissues. When we compare IDC to NBT, 12/17 IDC cases stained moderate or strong in C1QBP, showing a statistically significant difference in stain intensity (P≤0.01) between the two groups, while the percent of stained cells and location does not defer. There was no statistical difference in intensity, percent of stained cells or location between IBC and IDC samples for C1QBP, making C1QBP a protein that stains stronger in tumor tissue. IHC analysis shows that cytoplasmic L1CAM was expressed in 60% and 44% of IBC and IDC tumor tissues, respectively. Interestingly, 11% of IDC samples show expression of L1CAM in the membrane while no membranous expression was observed in IBCs. However, this cell adhesion molecule was not expressed in control samples vs IBC and IDC (P≤0.01). Our correlation analyses evidence that a positive relationship exists between L1CAM staining intensity and metastasis (P≤0.04; P≤0.02) in IBC patients

Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC Control

DC

C1QBP 10/10: Weak L1CAM 10/10: NS MCAM 10/10: Weak

MST1R 8/10: Weak 1/10: Moderate 1/10: NS MTDH 10/10: Moderate PLGRKT 10/10: Moderate SCAMP3 10/10: NS C1QBP 5/17: Weak 6/17: Moderate 6/17: Strong L1CAM 8/18: Weak 2/18: Moderate 6/18: NS 2/18: NTT MCAM 17/17: NS MST1R 12/17: Weak 3/17: Moderate 1/17: Strong 1/17: NS MTDH 7/17: Weak 9/17: Moderate 1/17: NTT

10/10: +++ _ 4/10: + 1/10: ++ 5/10: +++ 9/10: +++

10/10: C _ 9/10: C 1/10: MC

10/10: +++ 10/10: +++ _ 17/17: +++

10/10: NC 10/10: NC _ 17/17: C

4/18: + 5/18: ++ 1/18: +++

8/18: C 1/18: MC 1/18: M

_ 1/17: + 2/17: ++ 13/17: +++

_ 12/17: C 2/17: NC 2/17: MC

16/17: +++

16/17: C

9/10: NC

that stained weakly (P≤0.05) in more than 50% of cells (P≤0.05). Significant differences were observed in location in IBCs vs. IDCs (P≤0.01). Although membranous staining of MST1R was detected in IDCs, its main distribution was observed in the cytoplasm contrasting thereby with controls where all samples stained NC (P≤0.0001). Forty percent of IBC and IDC cases stained weakly for MTDH, while 100% of controls stained moderately (P≤0.01). IHC results showed a cytoplasmic distribution of MTDH in IBCs and IDCs but nuclear and cytoplasmic in control tissues (P≤ 0.0001). Staining intensity was significantly different between IBCs and IDCs (P≤0.05). Strong expression of MTDH was also observed in lymphatic vessels and in tumor emboli in IBC tissues (Figure 2B).

Twelve IDC tissues displayed weak staining of PLGRTK in comparison PLGRKT 12/17: Weak 2/17: + 9/17: C with IBCs and controls where 8/17 1/17: Moderate 2/17: ++ 4/17: NC displayed moderate and strong 4/17: NS 9/17: +++ staining (P≤0.01) and 10/10 moderSCAMP3 10/19: NS 5/17: + ate intensity (P≤0.0001), respec8/19: Weak 1/17: ++ 8/17: C tively. Moreover, a significant differ1/19: NTT 2/17: +++ ence in staining intensity was obNS = no staining, NTT = no tumor tissue. “+” = Less than 10% cells posiserved between IBC vs. controls tive staining, “++” = 10-50% cells positive staining, “+++” = More than (P≤0.01). IBC and IDC tissues dem50% cells positive staining. C, NC, MC, M means cytoplasmic, nuclear and onstrated cytoplasmic staining in cytoplasmic, membranous and cytoplasmic, and membranous, respectively. comparison with nuclear and cytoplasmic distribution in controls (P≤ 0.001). Weak staining of SCAMP3 was identi(Table 6). IHC results demonstrate that IBC fied in 86% of the IBC cases. SCAMP3 was MCAM-stained tissues display cytoplasmic or identified in the membrane and cytoplasm of membranous/cytoplasmic expression. Howtumor emboli cells and in lymphatic vessels ever, weak cytoplasmic expression of this protein was also detected in 100% of controls (Figure 2B), while cytoplasmic expression was (P≤0.001). Meanwhile, no expression of MCAM found in >40% of IDCs (P≤0.05). Interestingly, was detected in IDC tissues. Interestingly, a as shown in Figure 2A, no expression of this negative correlation between MCAM and lymprotein was detected in controls (vs. IBC, phatic invasion was observed in women with P≤0.0001, vs. IDC, P≤0.05). Therefore, our IBC (P≤0.04) (Table 6), suggesting that MCAM results suggest an important role for SCAMP3 might be acting as a tumor suppressor. Overall, in IBC invasion. these data suggest that L1CAM is a tumorInteraction analyses associated protein, while MCAM is negatively associated with lymphovascular invasion in IBC Functional networks analysis of upregulated patients. PMPs was performed using Ingenuity Pathway Nine cases of IBC stained moderate or strong Analysis. The top network functions identified for MST1R compared to 8/10 control cases as upregulated proteins in IBC cells were 1731

Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC

Figure 2. Protein expression and cellular distribution of selected proteins. A. Immunohistochemical analysis using antibodies against C1QBP, L1CAM, MCAM, MST1R, MTDH, PLGRTK and SCAMP3 in normal breast tissues (n=10), IBC (n=17) and IDC (n=24). B. MTDH and SCAMP3 expression in tumor emboli cells. Black arrows point to emboli. Micrographs were captured using an Olympus inverted microscope. Scale bar = 20 µm.

1732

Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC Table 6. Correlation analyses Protein L1CAM MCAM

Variables Staining vs metastasis Intensity vs metastasis Staining vs LI

Pearson’s r .556 .611 -.550

P-value 0.039 0.020 0.042

LI = Lymphatic Invasion.

proteins), Hereditary Disorder, Cellular Assembly, Organization, Function, Maintenance (13 proteins) and Gene Expression, RNA Damage and Repair, RNA Post-Transcriptional Modification (8 proteins) (Figures 3A, 4-6). These findings indicate that the SUM-149 cell PM proteome was mostly associated with cell morphology, organization and maintenance. Thus, the interaction potential of selected proteins was further analyzed. Interaction analysis identified direct and indirect relationships of eight verified proteins (C1QBP, FLOT1, ITGB5, L1CAM, MCAM, MST1R, MTDH, and SCAMP3) with central molecules that have an important role in breast cancer (EGFR, AKT and ERK). However, no direct or indirect interactions were found between PLGRKT and selected proteins or incorporated molecules into network (see Figure 6 for PLGRKT interacting proteins). This network shows the direct binding interaction between MST1R and EGFR and their capacity for AKT activation. Furthermore, C1QBP and SCAMP3 cause activation of AKT and EGFR, respectively (Figure 3B). Since, EGFR, AKT and ERK pathways are key for the IBC development and progression, the interaction of the validated proteins with these pathways suggest their potential role in IBC pathogenesis. Discussion

Figure 3. Functional network analysis of differentially upregulated PMPs. A. Top network functions identified as upregulated proteins in IBC cells. Network 1: Cell Morphology, Cellular Assembly and Organization, Immunological Disease (orange dots). Network 2: Hereditary Disorder, Cellular Assembly, Organization, Function, Maintenance (purple dots). Network 3: Gene Expression, RNA Damage and Repair, RNA Post-Transcriptional Modification (green dots). B. The image was created using the Ingenuity Pathways Analysis (IPA) platform (Ingenuity Systems; ©2000-2015 QIAGEN) by overlaying the PMPs detected by SILAC (red) onto a molecular network from the Ingenuity knowledgebase. Red indicates high SILAC ratios, and purple, yellow and blue indicates proteins that were not identified by SILAC but form part of this network. For each identified protein, the number corresponds to the protein quantification (log2 ratio). Legend indicates the function of each protein and the interactions between them.

involved in Cell Morphology, Cellular Assembly and Organization, Immunological Diseases (26 1733

Identification of PM-associated proteins is an important first step in the development of cancer-targeted therapies. In this report we quantify, identify and define for the first time the IBC membrane proteome. By comparing IBC cells with non-cancerous breast cancer cells using SILAC, we were able to identify strategies that IBC cells and tumors might use to proliferate, invade and progress to metastasis. Finally, we could establish similarities and differences between non-IBCs and IBCs comparing multiple breast cancer cell lines and tumors with different molecular profiles. Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC

Figure 4. Interactions between PMPs in network 1: Cell Morphology, Cellular Assembly and Organization, Immunological Disease. The image was created using the Ingenuity Pathways Analysis (IPA) platform (Ingenuity Systems; ©2000-2015 QIAGEN) by overlaying the membrane proteins detected by SILAC onto a molecular network from the Ingenuity knowledgebase. Red indicates high SILAC ratios, and gray indicates proteins that were not identified by SILAC but form part of this network. For each identified protein, the number corresponds to the protein quantification (log2 ratio).

SUM-149 and MCF-10A cells are the most used and well known models to examine the cell and molecular biology of IBC and non-cancerous mammary epithelia, respectively. However, some limitations exist in in vitro models, such as nutrient requirements or culture conditions. For this reason, using patient tumor tissues is the most accurate scenario to study protein expression and function. Since, tumors are influenced by a range of biological factors, which are necessary for tumor development and progression, using only the membrane fraction of cells to assess protein expression 1734

and quantification could be a limiting factor. It is well known that the selected proteins are PMPs or plasma membrane interacting proteins; however, IHC results also show expression of these proteins in other cell locations (i.e., cytoplasm or nucleus). This difference in location can be explained by the effect of several cellular stimuli in the tumor microenvironment. It is important to underline that we classified the protein expression distribution taking in consideration the location of the protein in the greatest number of stained cells. Here we discuss the distribution and the function of Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC

Figure 5. Interactions between PMPs in network 2: Hereditary Disorder, Cellular Assembly, Organization, Function, and Maintenance. The image was created using the IPA platform (Ingenuity Systems; ©2000-2015 QIAGEN) by overlaying the membrane proteins detected by SILAC onto a molecular network from the Ingenuity knowledgebase. Red indicates high SILAC ratios, and gray indicates proteins that were not identified by SILAC but form part of this network. For each identified protein, the number corresponds to the protein quantification (log2 ratio).

each protein in the different cell compartments. This is the first proteomic study where PLGRKT expression is described in IBC and non-IBC cells and tissues. PLGRKT is a novel integral membrane plasminogen receptor with an exposed C-terminal lysine to the cell surface which promotes plasminogen activation by the urokinase receptor and tissue plasminogen activator (uPA) [16]. PLGRKT is involved in regulation of inflammatory response and regulates monocyte/macrophage migration and matrix 1735

metalloproteinase activation [17]. Recent studies in IBC have evidenced that an increase in macrophage infiltration and an interaction with human monocytes promote tumor-progression and invasion of IBC [18-20]. The colocalization of the protease Cathepsin B, uPA and uPAR with caveolin-1 in the caveolae has been associated with metastasis to lymph nodes in IBC [21, 22]. Moreover, caveolin-1 overexpression mediates IBC cell invasion via AKT and RhoC GTPase [23]. Similarly, to caveolins, lipid rafts associated flotillins are involved in the transport of key molecules in breast cancer. Since, Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC

Figure 6. Interactions between PMPs in network 3: Gene Expression, RNA Damage and Repair, RNA Post-Transcriptional Modification. The image was created using the IPA platform (Ingenuity Systems; ©2000-2015 QIAGEN) by overlaying the membrane proteins detected by SILAC onto a molecular network from the Ingenuity knowledgebase. Red indicates high SILAC ratios, and gray indicates proteins that were not identified by SILAC but form part of this network. For each identified protein, the number corresponds to the protein quantification (log2 ratio).

IBC interaction with macrophages/monocytes and overexpression of caveolin-1, uPA and uPAR have been associated with invasion and metastasis and our data reveal the overexpression of flotilin-1 and PLGRKT is feasible to hypothesize that these proteins play an important the role in IBC progression. MCAM and L1CAM, have been associated with cancer progression via the activation of PI3K/ AKT and ERK signaling cascades [24]. Up1736

regulation of MCAM promotes motility, invasion, and tumorigenesis and is associated with a poor prognosis in breast cancer [25, 26]. Immunoblotting data showed MCAM overexpression in SUM-149 IBC cells similar to a published study [27]. Our IHC results show MCAM expression was detected in only 29% of IBC tissues. Although MCAM has been associated with oncogenesis, other studies show that its overexpression suppresses tumor growth establishing a controversial dual role [28, 29]. Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC Herein, 100% of NBTs express MCAM while IDCs did not show expression suggesting a tumor suppressor role. Interestingly, we demonstrated a negative relationship between MCAM and lymphovascular invasion in IBC patients. Since, this is the first study evaluating the expression of MCAM in IBC, further investigation using a larger subset of samples are necessary to elucidate the role of this protein in IBC. On the other hand, we validated published results that establish L1CAM overexpression in IBC [30]. This overexpression has been associated with IBC cell survival and invasion [31, 32]. Importantly, in this study we also evidenced that L1CAM expression correlates with metastasis establishment in women with IBC. Cleavage of the L1CAM ectodomain proximal to the PM is mediated by metalloproteinases yielding a C-terminal stub that is a γ-secretase substrate. This γ-secretase processed fragment results in the release of a soluble L1CAM intracellular domain into the cytoplasm, which has been implicated in breast cancer cell adhesion and migration [33, 34]. This phenomenon could explain our IHC results, which show expression of L1CAM at the cytoplasm in IBC instead of PM localization. The function of secretory carrier membrane protein (SCAMP3) has not been characterized in detail yet; however evidence demonstrates that it acts as a regulator of EGFR trafficking within endosomal membranes enhancing the recycling of the receptor and decreasing its degradation [35]. This is the first study where the protein expression of SCAMP3 has been assessed in breast cancer. Our findings demonstrate that SCAMP3 is expressed in almost 90% of IBC tissues, lymphatic vessels and tumor emboli cells. Although, further studies are necessary, SCAMP3 promises to be a molecular marker for the diagnosis or treatment of IBC. Membrane, nuclear and/or cytoplasmic Metadherin (MTDH) is overexpressed in about 45% of the primary tumors and is significantly correlated with clinical stage, tumor size, metastasis and poor survival through the activation of multiple oncogenic pathways such as PI3K/ AKT, Wnt/β-catenin and MAPK [36, 37]. In IBC, high ratios of HER2 transcripts were associated with increased proteomic levels of MTDH in SUM-190 cells [38]. Here, we show overexpression of MTDH in SUM-149 and KPL-4 IBC cells. 1737

Although, studies demonstrate that MTDH is expressed in low levels or is absent in most of normal human breast tissues [39], IHC data showed moderate nuclear and cytoplasmic expression in NBTs and strong cytoplasmic expression in IBCs, suggesting a redistribution of MTDH from nucleus to cytoplasm. Moreover, as well as SCAMP3, MTDH might be associated with lymphovascular invasion and metastasis in IBC. Our SILAC data revealed the overexpression of the receptor tyrosine kinase (RTK), macrophage-stimulating protein receptor. MST1R is a RTK of the c-Met family that activates several signaling cascades including RAS-ERK and PI3K-AKT. MST1R is overexpressed in approximately 50% of breast cancers and is associated with proliferation, metastasis and poor prognosis but barely detectable in normal breast epithelia [40, 41]. In accordance with previous findings, our results show weak expression in NBTs and overexpression in IBC cells and tissues. The MST1R precursor protein is synthesized as a single chain and remains in the cytoplasm where is cleaved to produce a functional heterodimer with other RTKs [42, 43]. MST1R/ MET crosstalk with β-catenin pathway facilitating its nuclear translocation leading in the transcription of oncogenic mRNAs [43]. Although we show membranous localization of MST1R, the most IBC tissues stained in the nucleus. Recent data suggest that MST1R/EGFR translocate to the nucleus, acting as a transcriptional regulator of c-JUN to promote survival of cancer cells in hypoxic conditions [44, 45]. In IBC, overexpression of eIF4G1 increases the translation of VEGF, which accounts for resistance to hypoxia required for IBC cell survival [46, 47]. We could hypothesize that the translocation of MST1R to the nucleus in IBC might play a role in IBC tumor survival under hypoxic conditions. Complement 1q binding protein (C1QBP), is mainly distributed in mitochondria but it can also be detected in the cytosol and cell surface by the activation of ERK [48]. Recently, elevated expression of cytoplasmic C1QBP was correlated with poor survival, lymphovascular invasion and metastasis to lymph nodes in breast and endometrial cancer patients [49, 50]. Our SILAC results show overexpression of C1QBP in SUM-149 IBC cells. Furthermore, IHC analysis demonstrated significantly elevated C1QBP protein levels in tumors and in accordance with Am J Cancer Res 2016;6(8):1720-1740

Plasma membrane proteome of IBC published studies its expression in IBC lymphatic vessels might be directly associated with lymphovascular invasion process.

Disclosure of conflict of interest

The present study is the first to identify the altered protein expression of membrane-proteins in IBC. The established proteomic differences between controls, non-IBCs and IBCs evidence the heterogeneity of breast cancer and suggest the use of diverse strategies for tumor formation and development. Furthermore, our data validate the central role of EGFR, AKT and ERK pathways in the oncogenic process of IBC and reveal the importance of continuing studies to assess the function of identified proteins in the localized cell compartments. Finally, we have presented potential biomarkers of IBC that will not only benefit accurate and early diagnosis of this intractable disease but also could be targets for further development of therapies.

Address correspondence to: Michelle M MartínezMontemayor, Universidad Central del Caribe - School of Medicine, P.O. Box 60327, Bayamon PR 009606032. E-mail: [email protected]

Acknowledgements We thank Dr. Scott Schaffer from the Proteomics and Mass Spectrometry Facility in UMASS for the MS analysis. We would like to acknowledge the UCC-SOM RCMI Common Instrumentation Area. This work was supported by National Cancer Institute #CA174307 (ISA), National Institute of General Medical Sciences #GM111171 (Martínez-Montemayor), National Center for Research Resources #RR003035, National Institute on Minority Health and Health Disparities #MD007583 (MMM, GMM), Na-tional Center for Research Resources #RR016470, GM103475 (UPR-pilot MartínezMontemayor), National Institute on Minority Health and Health Disparities #MD008149 (MMM), National Institute on Minority Health and Health Disparities #MD007587 (MMM), National Institute on Minority Health and Health Disparities #MD007600 (JPL), National Institute of General Medical Sciences #GM110513 (LAC), Title-V-PPOHA #P031M105050 and Title-V-Cooperative #P031S130068 U.S. Department of Education (LAC). New York State Stem Cell Program #CO28126, The Breast Cancer Research Foundation, National Cancer Institute CA178509, IBCRF (RJS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the U.S. Department of Education.

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